memory-engine

I’ve been working on the "Lost Context" problem in LLM-based agentic workflows. We all know the ceiling: as the conversation grows, the prompt becomes a graveyard of lost tokens or a massive, expensive compute bill. I’m open-sourcing the Memory Engine v3.1, a lightweight Python framework that implements Neural-Temporal Compression. Instead of just "summarizing" text, it treats data as a Hadamard Manifold, using a custom Tharyn Compression Scoring (TCS) system to decide what to persist and what to let decay. The Core Tech: Symbolic Tokenization: Hard-coded "Sacred Terms" (\phi, \psi) act as permanent anchors that bypass standard decay protocols. The Decay Floor: A hard-coded 0.05 threshold ensures that "Ancient Wisdom" (root context) is never purged, regardless of session length. TCS Grade Logic: A multi-weighted algorithm (0.45 Impor